Historically, when searching for information pertaining to a particular topic or task, a user would thumb through a paper index to locate the information, and then physically retrieve the information from another document. With the advent of computers and online search engines, searching for such information is becoming increasingly easy. For example, Google and other search engine inventions focus on the creation of a general index, which allows a user to input keywords to query the general index and hopefully retrieve relevant information. Google is a search engine produce by Google, Inc., located at 1600 Amphitheatre Parkway, Mountain View, Calif. 94043.
Some of these search engines also use a general ontology as an information retrieval method. General ontologies focus on building hierarchies of increasingly superordinate categories (e.g., a dimmer switch is a switch—is a kind of electronic device—is a device—is a thing). These general information retrieval methods focus on faster algorithms using general ontologies.
However, while functional for general searches, such search engines often provide invalid search results due to their generality. For example, should a user use a typical search engine to locate task-based information (e.g., information on how to complete a particular task), the user is likely to retrieve a plethora of irrelevant data, such as websites selling the items for use in the task.
To improve task-based search results, a continuing need exists for a system that focuses on task-based, relatively mutually-exclusive categories for enhancing the relevance of information requested by a trained expert for accomplishing a skilled task.